Read in the data

library(scran)
library(scater)
library(ggplot2)
library(DropletUtils)
library(openxlsx)
library(Rtsne)
library(stats)
library(knitr)
source("~/Dropbox/Postdoc/git/BE2020/Analysis/Functions/auxiliary.R")

Batch correction across tissues

I use data without patient 12 that might have gastric metaplasia samples.

sce.list <- readRDS("~/Dropbox/Postdoc/2019-12-29_BE2020/All_sce_filtered_high_quality.rds")
tissue.list <- list()

for(i in c("NE", "NG", "BE", "NSCJ", "BSCJ", "ND", "SMG")){
  
  cur_sces <- sce.list[which(grepl(i, names(sce.list)))]
  
  cur_sces <- lapply(cur_sces, function(n){
    rowData(n) <- rowData(n)[,1:2]
    n
  })
  
  #  cur_sces <-lapply(cur_sces, function (x) {
  #   if(colnames(rowData(x))[3] == "Type") {
  #     colnames(rowData(x))[3]<-"is_feature_control"
  #   } else {
  #     colnames(rowData(x))[3]<-colnames(rowData(x))[3]
  #   }
  #   x
  # })
  
  # Order sce object by size
  cur_sces <- cur_sces[order(unlist(lapply(cur_sces, ncol)), decreasing = TRUE)]
  
  
  
  # Combine sce objects
  tissue_sce <- do.call("cbind", cur_sces)
  qclusters <- quickCluster(tissue_sce)
  tissue_sce <- computeSumFactors(tissue_sce, clusters=qclusters)
  tissue_sce <- logNormCounts(tissue_sce)
  
  # Batch correction
  corrected <- batch.correction(cur_sces)
  
  # Save batch corrected counts in metdata
  metadata(tissue_sce)$corrected <- corrected
  
  # Compute tsne on corrected counts
  set.seed(1234)
  tsne <- Rtsne(t(corrected), pca = FALSE)
  
  # Store tsne in slot
  reducedDims(tissue_sce)$TSNE <- tsne$Y[,1:2]
  
  # Clustering on corrected data
  #dist.all <- as.dist(sqrt((1 - cor(as.matrix(corrected), 
  #                                    method = "spearman"))/2))
  #dendro <- hclust(dist.all, method = "ward.D2")
  #cluster <- as.character(cutreeDynamic(dendro = dendro, distM = as.matrix(dist.all), 
  #                      minClusterSize = 10, deepSplit = 0))
  if(i == "SMG") {
    g <- buildSNNGraph(corrected, k = 4, d = NA) # Use of k = 4 allows for capture of ductal cells as a seperate cluster in SMG samples. 
    cluster <- igraph::cluster_louvain(g)$membership
  } else {
    g <- buildSNNGraph(corrected, k = 5, d = NA) # A good balance needed to separate the samples.
    cluster <- igraph::cluster_louvain(g)$membership
  }

  # Save clustering in new slot
  colData(tissue_sce)$Tissue_cluster <- cluster
  
  # Visualize clustering
  cur_p <- ggplot(data.frame(tSNE1 = reducedDims(tissue_sce)$TSNE[,1],
                             tSNE2 = reducedDims(tissue_sce)$TSNE[,2],
                             clusters = as.factor(colData(tissue_sce)$Tissue_cluster))) +
    geom_point(aes(tSNE1, tSNE2, colour = clusters))+ggtitle(i)
  print(cur_p)
  
  
  cur_p <- ggplot(data.frame(tSNE1 = reducedDims(tissue_sce)$TSNE[,1],
                             tSNE2 = reducedDims(tissue_sce)$TSNE[,2],
                             clusters = as.factor(colData(tissue_sce)$Patient))) +
    geom_point(aes(tSNE1, tSNE2, colour = clusters))+ggtitle(i)
  print(cur_p)
  
  # Visualize whether populations are patient specific 
  print(kable(table(cluster, colData(tissue_sce)$Patient),row.names = TRUE))
  cat("\n")
  
  
  # Perform differential expression
  markers <- findMarkers(tissue_sce, groups = cluster, block = colData(tissue_sce)$Patient)
  
  
  markers.spec <- lapply(markers, function(n){
    if(!is.na(n$Top[1]) & !is.nan(sum(as.matrix(n[1,3:ncol(n)])))){
      test_n <- !is.na(n[1,3:ncol(n)])[1,]
      cur_n <- n[n$FDR < 0.1 & apply(n[,3:ncol(n)], 1, function(x){sum(x[test_n] > 0)}) == sum(test_n),]
      if(nrow(cur_n) > 0){
        cur_n$GeneName <- rowData(tissue_sce)$Symbol[match(rownames(cur_n), rowData(tissue_sce)$ID)]
      }
    }
    else{
      cur_n <- NULL
    }
    cur_n
  })
  
  if(!dir.exists(paste("~/Dropbox/Postdoc/2019-12-29_BE2020/Results/Marker_genes/Tissues_filtered/", 
                       i, sep = ""))){
    dir.create(paste("~/Dropbox/Postdoc/2019-12-29_BE2020/Results/Marker_genes/Tissues_filtered/", 
                     i, sep = ""))
  }
  
  write.xlsx(markers.spec, paste("~/Dropbox/Postdoc/2019-12-29_BE2020/Results/Marker_genes/Tissues_filtered/", i, "/Marker_genes.xlsx", sep = ""))
  
  tissue.list[[i]] <- tissue_sce
}

	Patient01	Patient02	Patient03	Patient05	Patient06	Patient07	Patient08	Patient09	Patient10
1	14	86	3	6	0	2	4	14	11
2	184	0	0	4	105	0	0	0	0
3	128	23	17	28	47	27	34	52	27
4	268	497	0	143	13	0	43	40	554
5	109	731	274	98	0	21	36	8	123
6	184	656	66	239	0	90	137	32	95
7	12	73	0	48	0	0	2	1	68
8	477	418	17	152	111	2	41	87	286
9	187	652	30	124	1	5	102	64	399
10	98	347	59	174	0	193	68	6	53
11	107	176	15	119	18	45	36	88	83
12	358	925	67	196	2	48	192	115	236
13	203	654	47	229	1	64	126	35	85
14	91	225	76	115	0	117	35	2	36
15	133	115	66	198	14	176	56	47	42
16	386	7	0	52	404	208	3	54	3
17	22	26	12	3	17	0	6	14	2

	Patient01	Patient02	Patient03	Patient06	Patient07	Patient08	Patient09	Patient10	Patient13
1	3	44	49	0	1	0	0	4	233
2	3	20	2	2	7	2	2	1	2
3	26	145	50	11	46	7	28	18	86
4	0	0	0	0	0	0	0	7	7
5	14	90	71	5	28	10	23	8	36
6	0	4	2	0	5	3	1	1	23
7	22	90	8	6	7	4	5	6	93
8	39	106	33	16	43	12	15	15	99
9	0	10	0	3	50	4	16	1	65
10	0	28	0	1	17	3	14	1	28
11	4	63	20	7	64	4	30	12	11
12	2	3	2	0	13	2	0	4	0
13	0	3	2	0	18	2	0	7	0
14	0	46	5	0	0	0	0	2	69
15	3	9	8	0	3	0	2	2	1
16	0	1	9	2	0	0	1	0	0
17	32	199	73	23	81	7	37	16	72
18	2	6	7	0	21	6	7	0	2
19	6	14	11	3	92	14	27	18	18
20	1	1	0	0	7	4	0	2	1
21	4	162	39	1	52	35	0	9	20
22	3	3	3	0	37	12	1	3	0
23	25	233	110	14	7	4	8	2	32

## Warning in .pairwise_blocked_template(x, clust.vals, nblocks, direction =
## direction, : no within-block comparison between 16 and 4

## Warning in .pairwise_blocked_template(x, clust.vals, nblocks, direction =
## direction, : no within-block comparison between 16 and 10

## Warning in .pairwise_blocked_template(x, clust.vals, nblocks, direction =
## direction, : no within-block comparison between 20 and 16

	Patient03	Patient07	Patient09
1	5	23	0
2	0	82	0
3	1	39	2
4	2	27	1
5	4	262	0
6	61	98	54
7	2	126	11
8	1	174	0
9	75	52	21
10	37	43	20
11	12	81	7
12	71	35	31
13	75	95	57
14	54	22	17
15	165	33	18
16	51	44	12
17	88	67	41
18	6	32	32
19	27	113	37

	Patient01	Patient02	Patient04	Patient05	Patient06	Patient08	Patient10	Patient13
1	0	9	237	74	0	4	51	107
2	0	11	74	100	47	43	30	42
3	0	2	267	153	71	77	47	321
4	0	7	111	223	48	39	28	48
5	8	229	3	0	36	34	37	13
6	26	503	12	2	30	43	23	19
7	38	505	7	0	11	27	43	15
8	62	481	3	3	1	17	16	4
9	0	8	410	70	12	44	31	126
10	0	42	140	117	42	56	23	345
11	0	29	367	102	15	69	34	214
12	0	0	80	0	8	0	21	103
13	0	0	112	51	32	1	92	31
14	0	7	3	0	38	0	13	92
15	0	1	166	17	29	2	17	41
16	0	5	138	140	26	176	6	479
17	1	4	16	0	62	8	5	33
18	0	2	16	15	418	10	14	77
19	0	59	22	67	57	43	72	155
20	0	0	20	7	113	0	41	3
21	0	18	104	57	58	24	111	154
22	0	1	0	1	1	1	36	4
23	2	7	42	0	39	16	16	28
24	0	1	271	143	40	33	50	158
25	1	0	24	0	12	2	1	14
26	15	83	5	2	4	29	9	0

	Patient03	Patient07	Patient09
1	63	25	39
2	6	3	0
3	135	16	47
4	68	3	14
5	108	44	17
6	234	2	3
7	130	59	11
8	26	122	5
9	5	115	21
10	51	58	26
11	30	79	30
12	27	71	20
13	1	5	1
14	88	32	33
15	3	13	6
16	24	7	39
17	9	18	6
18	50	24	34
19	18	23	130
20	11	27	2
21	4	18	45

	Patient07	Patient08	Patient09	Patient12
1	0	7	0	7
2	1	4	1	7
3	0	6	0	4
4	68	9	34	211
5	15	14	35	116
6	25	75	53	157
7	32	16	19	64
8	18	53	27	64
9	75	139	62	159
10	17	28	14	48
11	51	4	17	50
12	71	112	39	111
13	5	0	0	2
14	30	11	11	22
15	5	3	13	17
16	51	12	22	138
17	24	101	87	145
18	22	155	21	178
19	50	50	77	141
20	10	7	13	12

	Patient10	Patient11	Patient13
1	0	6	1
2	61	163	1
3	15	126	5
4	0	67	0
5	2	81	11
6	63	187	3
7	59	126	0
8	10	57	0
9	3	57	0
10	162	46	37
11	23	46	0
12	17	208	0
13	51	232	0
14	227	20	23
15	41	118	5
16	18	41	105
17	26	32	0
18	12	122	0
19	30	70	72
20	1	106	120

saveRDS(tissue.list, "~/Dropbox/Postdoc/2019-12-29_BE2020/Tissue_sce_filtered.rds")

End Matter

To finish get session info:

sessionInfo()

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora 31 (Workstation Edition)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] destiny_3.0.1               edgeR_3.28.0               
##  [3] limma_3.42.2                dbscan_1.1-5               
##  [5] princurve_2.1.4             dynamicTreeCut_1.63-1      
##  [7] knitr_1.28                  Rtsne_0.15                 
##  [9] openxlsx_4.1.4              DropletUtils_1.6.1         
## [11] scater_1.14.6               ggplot2_3.2.1              
## [13] scran_1.14.6                SingleCellExperiment_1.8.0 
## [15] SummarizedExperiment_1.16.1 DelayedArray_0.12.2        
## [17] BiocParallel_1.20.1         matrixStats_0.55.0         
## [19] Biobase_2.46.0              GenomicRanges_1.38.0       
## [21] GenomeInfoDb_1.22.0         IRanges_2.20.2             
## [23] S4Vectors_0.24.3            BiocGenerics_0.32.0        
## 
## loaded via a namespace (and not attached):
##   [1] ggbeeswarm_0.6.0         colorspace_1.4-1         RcppEigen_0.3.3.7.0     
##   [4] class_7.3-15             rio_0.5.16               XVector_0.26.0          
##   [7] RcppHNSW_0.2.0           BiocNeighbors_1.4.1      proxy_0.4-23            
##  [10] farver_2.0.3             hexbin_1.28.1            RSpectra_0.16-0         
##  [13] ranger_0.12.1            codetools_0.2-16         R.methodsS3_1.8.0       
##  [16] robustbase_0.93-5        R.oo_1.23.0              HDF5Array_1.14.2        
##  [19] compiler_3.6.2           ggplot.multistats_1.0.0  dqrng_0.2.1             
##  [22] assertthat_0.2.1         Matrix_1.2-18            lazyeval_0.2.2          
##  [25] formatR_1.7              BiocSingular_1.2.1       htmltools_0.4.0         
##  [28] tools_3.6.2              rsvd_1.0.2               igraph_1.2.4.2          
##  [31] gtable_0.3.0             glue_1.3.1               GenomeInfoDbData_1.2.2  
##  [34] dplyr_0.8.4              ggthemes_4.2.0           Rcpp_1.0.3              
##  [37] carData_3.0-3            cellranger_1.1.0         vctrs_0.2.2             
##  [40] DelayedMatrixStats_1.8.0 lmtest_0.9-37            xfun_0.12               
##  [43] laeken_0.5.1             stringr_1.4.0            lifecycle_0.1.0         
##  [46] irlba_2.3.3              statmod_1.4.33           DEoptimR_1.0-8          
##  [49] zlibbioc_1.32.0          MASS_7.3-51.4            zoo_1.8-7               
##  [52] scales_1.1.0             VIM_5.1.0                pcaMethods_1.78.0       
##  [55] hms_0.5.3                rhdf5_2.30.1             yaml_2.2.1              
##  [58] curl_4.3                 gridExtra_2.3            stringi_1.4.5           
##  [61] highr_0.8                e1071_1.7-3              TTR_0.23-6              
##  [64] boot_1.3-23              zip_2.0.4                rlang_0.4.4             
##  [67] pkgconfig_2.0.3          bitops_1.0-6             evaluate_0.14           
##  [70] lattice_0.20-38          purrr_0.3.3              Rhdf5lib_1.8.0          
##  [73] labeling_0.3             tidyselect_1.0.0         magrittr_1.5            
##  [76] R6_2.4.1                 pillar_1.4.3             haven_2.2.0             
##  [79] foreign_0.8-72           withr_2.1.2              xts_0.12-0              
##  [82] scatterplot3d_0.3-41     abind_1.4-5              RCurl_1.98-1.1          
##  [85] sp_1.3-2                 nnet_7.3-12              tibble_2.1.3            
##  [88] batchelor_1.2.4          crayon_1.3.4             car_3.0-6               
##  [91] rmarkdown_2.1            viridis_0.5.1            locfit_1.5-9.1          
##  [94] grid_3.6.2               readxl_1.3.1             data.table_1.12.8       
##  [97] forcats_0.4.0            vcd_1.4-5                digest_0.6.24           
## [100] tidyr_1.0.2              R.utils_2.9.2            munsell_0.5.0           
## [103] beeswarm_0.2.3           viridisLite_0.3.0        smoother_1.1            
## [106] vipor_0.4.5

Batch correction across tissues

Karol Nowicki-Osuch, based on Nils Eling

2020-03-10

Read in the data

Batch correction across tissues

End Matter

	Patient01	Patient02	Patient03	Patient05	Patient06	Patient07	Patient08	Patient09	Patient10
1	14	86	3	6	0	2	4	14	11
2	184	0	0	4	105	0	0	0	0
3	128	23	17	28	47	27	34	52	27
4	268	497	0	143	13	0	43	40	554
5	109	731	274	98	0	21	36	8	123
6	184	656	66	239	0	90	137	32	95
7	12	73	0	48	0	0	2	1	68
8	477	418	17	152	111	2	41	87	286
9	187	652	30	124	1	5	102	64	399
10	98	347	59	174	0	193	68	6	53
11	107	176	15	119	18	45	36	88	83
12	358	925	67	196	2	48	192	115	236
13	203	654	47	229	1	64	126	35	85
14	91	225	76	115	0	117	35	2	36
15	133	115	66	198	14	176	56	47	42
16	386	7	0	52	404	208	3	54	3
17	22	26	12	3	17	0	6	14	2

	Patient01	Patient02	Patient03	Patient06	Patient07	Patient08	Patient09	Patient10	Patient13
1	3	44	49	0	1	0	0	4	233
2	3	20	2	2	7	2	2	1	2
3	26	145	50	11	46	7	28	18	86
4	0	0	0	0	0	0	0	7	7
5	14	90	71	5	28	10	23	8	36
6	0	4	2	0	5	3	1	1	23
7	22	90	8	6	7	4	5	6	93
8	39	106	33	16	43	12	15	15	99
9	0	10	0	3	50	4	16	1	65
10	0	28	0	1	17	3	14	1	28
11	4	63	20	7	64	4	30	12	11
12	2	3	2	0	13	2	0	4	0
13	0	3	2	0	18	2	0	7	0
14	0	46	5	0	0	0	0	2	69
15	3	9	8	0	3	0	2	2	1
16	0	1	9	2	0	0	1	0	0
17	32	199	73	23	81	7	37	16	72
18	2	6	7	0	21	6	7	0	2
19	6	14	11	3	92	14	27	18	18
20	1	1	0	0	7	4	0	2	1
21	4	162	39	1	52	35	0	9	20
22	3	3	3	0	37	12	1	3	0
23	25	233	110	14	7	4	8	2	32

	Patient03	Patient07	Patient09
1	5	23	0
2	0	82	0
3	1	39	2
4	2	27	1
5	4	262	0
6	61	98	54
7	2	126	11
8	1	174	0
9	75	52	21
10	37	43	20
11	12	81	7
12	71	35	31
13	75	95	57
14	54	22	17
15	165	33	18
16	51	44	12
17	88	67	41
18	6	32	32
19	27	113	37

	Patient01	Patient02	Patient04	Patient05	Patient06	Patient08	Patient10	Patient13
1	0	9	237	74	0	4	51	107
2	0	11	74	100	47	43	30	42
3	0	2	267	153	71	77	47	321
4	0	7	111	223	48	39	28	48
5	8	229	3	0	36	34	37	13
6	26	503	12	2	30	43	23	19
7	38	505	7	0	11	27	43	15
8	62	481	3	3	1	17	16	4
9	0	8	410	70	12	44	31	126
10	0	42	140	117	42	56	23	345
11	0	29	367	102	15	69	34	214
12	0	0	80	0	8	0	21	103
13	0	0	112	51	32	1	92	31
14	0	7	3	0	38	0	13	92
15	0	1	166	17	29	2	17	41
16	0	5	138	140	26	176	6	479
17	1	4	16	0	62	8	5	33
18	0	2	16	15	418	10	14	77
19	0	59	22	67	57	43	72	155
20	0	0	20	7	113	0	41	3
21	0	18	104	57	58	24	111	154
22	0	1	0	1	1	1	36	4
23	2	7	42	0	39	16	16	28
24	0	1	271	143	40	33	50	158
25	1	0	24	0	12	2	1	14
26	15	83	5	2	4	29	9	0

	Patient03	Patient07	Patient09
1	63	25	39
2	6	3	0
3	135	16	47
4	68	3	14
5	108	44	17
6	234	2	3
7	130	59	11
8	26	122	5
9	5	115	21
10	51	58	26
11	30	79	30
12	27	71	20
13	1	5	1
14	88	32	33
15	3	13	6
16	24	7	39
17	9	18	6
18	50	24	34
19	18	23	130
20	11	27	2
21	4	18	45

	Patient07	Patient08	Patient09	Patient12
1	0	7	0	7
2	1	4	1	7
3	0	6	0	4
4	68	9	34	211
5	15	14	35	116
6	25	75	53	157
7	32	16	19	64
8	18	53	27	64
9	75	139	62	159
10	17	28	14	48
11	51	4	17	50
12	71	112	39	111
13	5	0	0	2
14	30	11	11	22
15	5	3	13	17
16	51	12	22	138
17	24	101	87	145
18	22	155	21	178
19	50	50	77	141
20	10	7	13	12

	Patient10	Patient11	Patient13
1	0	6	1
2	61	163	1
3	15	126	5
4	0	67	0
5	2	81	11
6	63	187	3
7	59	126	0
8	10	57	0
9	3	57	0
10	162	46	37
11	23	46	0
12	17	208	0
13	51	232	0
14	227	20	23
15	41	118	5
16	18	41	105
17	26	32	0
18	12	122	0
19	30	70	72
20	1	106	120

	Patient01	Patient02	Patient03	Patient05	Patient06	Patient07	Patient08	Patient09	Patient10
1	14	86	3	6	0	2	4	14	11
2	184	0	0	4	105	0	0	0	0
3	128	23	17	28	47	27	34	52	27
4	268	497	0	143	13	0	43	40	554
5	109	731	274	98	0	21	36	8	123
6	184	656	66	239	0	90	137	32	95
7	12	73	0	48	0	0	2	1	68
8	477	418	17	152	111	2	41	87	286
9	187	652	30	124	1	5	102	64	399
10	98	347	59	174	0	193	68	6	53
11	107	176	15	119	18	45	36	88	83
12	358	925	67	196	2	48	192	115	236
13	203	654	47	229	1	64	126	35	85
14	91	225	76	115	0	117	35	2	36
15	133	115	66	198	14	176	56	47	42
16	386	7	0	52	404	208	3	54	3
17	22	26	12	3	17	0	6	14	2

	Patient01	Patient02	Patient03	Patient06	Patient07	Patient08	Patient09	Patient10	Patient13
1	3	44	49	0	1	0	0	4	233
2	3	20	2	2	7	2	2	1	2
3	26	145	50	11	46	7	28	18	86
4	0	0	0	0	0	0	0	7	7
5	14	90	71	5	28	10	23	8	36
6	0	4	2	0	5	3	1	1	23
7	22	90	8	6	7	4	5	6	93
8	39	106	33	16	43	12	15	15	99
9	0	10	0	3	50	4	16	1	65
10	0	28	0	1	17	3	14	1	28
11	4	63	20	7	64	4	30	12	11
12	2	3	2	0	13	2	0	4	0
13	0	3	2	0	18	2	0	7	0
14	0	46	5	0	0	0	0	2	69
15	3	9	8	0	3	0	2	2	1
16	0	1	9	2	0	0	1	0	0
17	32	199	73	23	81	7	37	16	72
18	2	6	7	0	21	6	7	0	2
19	6	14	11	3	92	14	27	18	18
20	1	1	0	0	7	4	0	2	1
21	4	162	39	1	52	35	0	9	20
22	3	3	3	0	37	12	1	3	0
23	25	233	110	14	7	4	8	2	32

	Patient03	Patient07	Patient09
1	5	23	0
2	0	82	0
3	1	39	2
4	2	27	1
5	4	262	0
6	61	98	54
7	2	126	11
8	1	174	0
9	75	52	21
10	37	43	20
11	12	81	7
12	71	35	31
13	75	95	57
14	54	22	17
15	165	33	18
16	51	44	12
17	88	67	41
18	6	32	32
19	27	113	37

	Patient01	Patient02	Patient04	Patient05	Patient06	Patient08	Patient10	Patient13
1	0	9	237	74	0	4	51	107
2	0	11	74	100	47	43	30	42
3	0	2	267	153	71	77	47	321
4	0	7	111	223	48	39	28	48
5	8	229	3	0	36	34	37	13
6	26	503	12	2	30	43	23	19
7	38	505	7	0	11	27	43	15
8	62	481	3	3	1	17	16	4
9	0	8	410	70	12	44	31	126
10	0	42	140	117	42	56	23	345
11	0	29	367	102	15	69	34	214
12	0	0	80	0	8	0	21	103
13	0	0	112	51	32	1	92	31
14	0	7	3	0	38	0	13	92
15	0	1	166	17	29	2	17	41
16	0	5	138	140	26	176	6	479
17	1	4	16	0	62	8	5	33
18	0	2	16	15	418	10	14	77
19	0	59	22	67	57	43	72	155
20	0	0	20	7	113	0	41	3
21	0	18	104	57	58	24	111	154
22	0	1	0	1	1	1	36	4
23	2	7	42	0	39	16	16	28
24	0	1	271	143	40	33	50	158
25	1	0	24	0	12	2	1	14
26	15	83	5	2	4	29	9	0

	Patient03	Patient07	Patient09
1	63	25	39
2	6	3	0
3	135	16	47
4	68	3	14
5	108	44	17
6	234	2	3
7	130	59	11
8	26	122	5
9	5	115	21
10	51	58	26
11	30	79	30
12	27	71	20
13	1	5	1
14	88	32	33
15	3	13	6
16	24	7	39
17	9	18	6
18	50	24	34
19	18	23	130
20	11	27	2
21	4	18	45

	Patient07	Patient08	Patient09	Patient12
1	0	7	0	7
2	1	4	1	7
3	0	6	0	4
4	68	9	34	211
5	15	14	35	116
6	25	75	53	157
7	32	16	19	64
8	18	53	27	64
9	75	139	62	159
10	17	28	14	48
11	51	4	17	50
12	71	112	39	111
13	5	0	0	2
14	30	11	11	22
15	5	3	13	17
16	51	12	22	138
17	24	101	87	145
18	22	155	21	178
19	50	50	77	141
20	10	7	13	12

	Patient10	Patient11	Patient13
1	0	6	1
2	61	163	1
3	15	126	5
4	0	67	0
5	2	81	11
6	63	187	3
7	59	126	0
8	10	57	0
9	3	57	0
10	162	46	37
11	23	46	0
12	17	208	0
13	51	232	0
14	227	20	23
15	41	118	5
16	18	41	105
17	26	32	0
18	12	122	0
19	30	70	72
20	1	106	120

	Patient01	Patient02	Patient03	Patient05	Patient06	Patient07	Patient08	Patient09	Patient10
1	14	86	3	6	0	2	4	14	11
2	184	0	0	4	105	0	0	0	0
3	128	23	17	28	47	27	34	52	27
4	268	497	0	143	13	0	43	40	554
5	109	731	274	98	0	21	36	8	123
6	184	656	66	239	0	90	137	32	95
7	12	73	0	48	0	0	2	1	68
8	477	418	17	152	111	2	41	87	286
9	187	652	30	124	1	5	102	64	399
10	98	347	59	174	0	193	68	6	53
11	107	176	15	119	18	45	36	88	83
12	358	925	67	196	2	48	192	115	236
13	203	654	47	229	1	64	126	35	85
14	91	225	76	115	0	117	35	2	36
15	133	115	66	198	14	176	56	47	42
16	386	7	0	52	404	208	3	54	3
17	22	26	12	3	17	0	6	14	2

	Patient01	Patient02	Patient03	Patient06	Patient07	Patient08	Patient09	Patient10	Patient13
1	3	44	49	0	1	0	0	4	233
2	3	20	2	2	7	2	2	1	2
3	26	145	50	11	46	7	28	18	86
4	0	0	0	0	0	0	0	7	7
5	14	90	71	5	28	10	23	8	36
6	0	4	2	0	5	3	1	1	23
7	22	90	8	6	7	4	5	6	93
8	39	106	33	16	43	12	15	15	99
9	0	10	0	3	50	4	16	1	65
10	0	28	0	1	17	3	14	1	28
11	4	63	20	7	64	4	30	12	11
12	2	3	2	0	13	2	0	4	0
13	0	3	2	0	18	2	0	7	0
14	0	46	5	0	0	0	0	2	69
15	3	9	8	0	3	0	2	2	1
16	0	1	9	2	0	0	1	0	0
17	32	199	73	23	81	7	37	16	72
18	2	6	7	0	21	6	7	0	2
19	6	14	11	3	92	14	27	18	18
20	1	1	0	0	7	4	0	2	1
21	4	162	39	1	52	35	0	9	20
22	3	3	3	0	37	12	1	3	0
23	25	233	110	14	7	4	8	2	32

	Patient03	Patient07	Patient09
1	5	23	0
2	0	82	0
3	1	39	2
4	2	27	1
5	4	262	0
6	61	98	54
7	2	126	11
8	1	174	0
9	75	52	21
10	37	43	20
11	12	81	7
12	71	35	31
13	75	95	57
14	54	22	17
15	165	33	18
16	51	44	12
17	88	67	41
18	6	32	32
19	27	113	37

	Patient01	Patient02	Patient04	Patient05	Patient06	Patient08	Patient10	Patient13
1	0	9	237	74	0	4	51	107
2	0	11	74	100	47	43	30	42
3	0	2	267	153	71	77	47	321
4	0	7	111	223	48	39	28	48
5	8	229	3	0	36	34	37	13
6	26	503	12	2	30	43	23	19
7	38	505	7	0	11	27	43	15
8	62	481	3	3	1	17	16	4
9	0	8	410	70	12	44	31	126
10	0	42	140	117	42	56	23	345
11	0	29	367	102	15	69	34	214
12	0	0	80	0	8	0	21	103
13	0	0	112	51	32	1	92	31
14	0	7	3	0	38	0	13	92
15	0	1	166	17	29	2	17	41
16	0	5	138	140	26	176	6	479
17	1	4	16	0	62	8	5	33
18	0	2	16	15	418	10	14	77
19	0	59	22	67	57	43	72	155
20	0	0	20	7	113	0	41	3
21	0	18	104	57	58	24	111	154
22	0	1	0	1	1	1	36	4
23	2	7	42	0	39	16	16	28
24	0	1	271	143	40	33	50	158
25	1	0	24	0	12	2	1	14
26	15	83	5	2	4	29	9	0

	Patient03	Patient07	Patient09
1	63	25	39
2	6	3	0
3	135	16	47
4	68	3	14
5	108	44	17
6	234	2	3
7	130	59	11
8	26	122	5
9	5	115	21
10	51	58	26
11	30	79	30
12	27	71	20
13	1	5	1
14	88	32	33
15	3	13	6
16	24	7	39
17	9	18	6
18	50	24	34
19	18	23	130
20	11	27	2
21	4	18	45

	Patient07	Patient08	Patient09	Patient12
1	0	7	0	7
2	1	4	1	7
3	0	6	0	4
4	68	9	34	211
5	15	14	35	116
6	25	75	53	157
7	32	16	19	64
8	18	53	27	64
9	75	139	62	159
10	17	28	14	48
11	51	4	17	50
12	71	112	39	111
13	5	0	0	2
14	30	11	11	22
15	5	3	13	17
16	51	12	22	138
17	24	101	87	145
18	22	155	21	178
19	50	50	77	141
20	10	7	13	12

	Patient10	Patient11	Patient13
1	0	6	1
2	61	163	1
3	15	126	5
4	0	67	0
5	2	81	11
6	63	187	3
7	59	126	0
8	10	57	0
9	3	57	0
10	162	46	37
11	23	46	0
12	17	208	0
13	51	232	0
14	227	20	23
15	41	118	5
16	18	41	105
17	26	32	0
18	12	122	0
19	30	70	72
20	1	106	120